Evidence map›Paper›PMID 36940325›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Associative learning in the cnidarian

Gaelle Botton-Amiot, Pedro Martinez, Simon G Sprecher

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
20.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. The maps of meaning consciousness theory.Frontiers in psychology · 2024
    Article
  7. Self-Organisation of Prediction Models.Entropy (Basel, Switzerland) · 2023
    Review
  8. Article
  9. Associative learning in the cnidarianProceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  10. Learning in Cnidaria: a summary.Communicative & integrative biology · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Gaelle Botton-AmiotDepartment of Biology, Institute of Zoology, University of Fribourg, CH-1700 Fribourg, Switzerland.ORCID 0000-0002-5353-6912
Pedro MartinezDepartament de Genètica, Universitat de Barcelona, 08028 Barcelona, Spain.ORCID 0000-0003-3956-7541
Simon G SprecherDepartment of Biology, Institute of Zoology, University of Fribourg, CH-1700 Fribourg, Switzerland.ORCID 0000-0001-9060-3750
University of Fribourg · CHInstitució Catalana de Recerca i Estudis Avançats · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to learn and form memories allows animals to adapt their behavior based on previous experiences. Associative learning, the process through which organisms learn about the relationship between two distinct events, has been extensively studied in various animal taxa. However, the existence of associative learning, prior to the emergence of centralized nervous systems in bilaterian animals, remains unclear. Cnidarians such as sea anemones or jellyfish possess a nerve net, which lacks centralization. As the sister group to bilaterians, they are particularly well suited for studying the evolution of nervous system functions. Here, we probe the capacity of the starlet sea anemone

Indexed as

Sea AnemonesAnimalsConditioning, ClassicalNervous Systemassociative learningcnidariacognitionlearningnervous system evolution

Identifiers

PMID36940325
PMCPMC10068830
OpenAlexW4327913913

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.